Ultrasound (US) is widely accessible and radiation-free but has a steep learning curve due to its dynamic nature and non-standard imaging planes. Additionally, the constant need to shift focus between the US screen and the patient poses a challenge. To address these issues, we integrate deep learning (DL)-based semantic segmentation for real-time (RT) automated kidney volumetric measurements, which are essential for clinical assessment but are traditionally time-consuming and prone to fatigue. This automation allows clinicians to concentrate on image interpretation rather than manual measurements. Complementing DL, augmented reality (AR) enhances the usability of US by projecting the display directly into the clinician's field of view, improving ergonomics and reducing the cognitive load associated with screen-to-patient transitions. Two AR-DL-assisted US pipelines on HoloLens-2 are proposed: one streams directly via the application programming interface for a wireless setup, while the other supports any US device with video output for broader accessibility. We evaluate RT feasibility and accuracy using the Open Kidney Dataset and open-source segmentation models (nnU-Net, Segmenter, YOLO with MedSAM and LiteMedSAM). Our open-source GitHub pipeline includes model implementations, measurement algorithms, and a Wi-Fi-based streaming solution, enhancing US training and diagnostics, especially in point-of-care settings.

Congenital uterine anomalies (CUAs) can lead to infertility, miscarriage, preterm birth, and an increased risk of pregnancy complications. Compared to traditional 2D ultrasound (US), 3D US can reconstruct the coronal plane, providing a clear visualization of the uterine morphology for assessing CUAs accurately. In this paper, we propose an intelligent system for simultaneous automated plane localization and CUA diagnosis. Our highlights are: 1) we develop a denoising diffusion model with local (plane) and global (volume/text) guidance, using an adaptive weighting strategy to optimize attention allocation to different conditions; 2) we introduce a reinforcement learning-based framework with unsupervised rewards to extract the key slice summary from redundant sequences, fully integrating information across multiple planes to reduce learning difficulty; 3) we provide text-driven uncertainty modeling for coarse prediction, and leverage it to adjust the classification probability for overall performance improvement. Extensive experiments on a large 3D uterine US dataset show the efficacy of our method, in terms of plane localization and CUA diagnosis. Code is available at https://github.com/yuhoo0302/CUA-US.

Accurate carotid plaque grading (CPG) is vital to assess the risk of cardiovascular and cerebrovascular diseases. Due to the small size and high intra-class variability of plaque, CPG is commonly evaluated using a combination of transverse and longitudinal ultrasound views in clinical practice. However, most existing deep learning-based multi-view classification methods focus on feature fusion across different views, neglecting the importance of representation learning and the difference in class features. To address these issues, we propose a novel Corpus-View-Category Refinement Framework (CVC-RF) that processes information from Corpus-, View-, and Category-levels, enhancing model performance. Our contribution is four-fold. First, to the best of our knowledge, we are the foremost deep learning-based method for CPG according to the latest Carotid Plaque-RADS guidelines. Second, we propose a novel center-memory contrastive loss, which enhances the network's global modeling capability by comparing with representative cluster centers and diverse negative samples at the Corpus level. Third, we design a cascaded down-sampling attention module to fuse multi-scale information and achieve implicit feature interaction at the View level. Finally, a parameter-free mixture-of-experts weighting strategy is introduced to leverage class clustering knowledge to weight different experts, enabling feature decoupling at the Category level. Experimental results indicate that CVC-RF effectively models global features via multi-level refinement, achieving state-of-the-art performance in the challenging CPG task.

Federated learning (FL) has emerged as a promising paradigm for collaboratively training deep learning models across institutions without exchanging sensitive medical data. However, its effectiveness is often hindered by limited data availability and non-independent, identically distributed data across participating clients, which can degrade model performance and generalization. To address these challenges, we propose a generative AI based data augmentation framework that integrates synthetic image sharing into the federated training process for breast cancer diagnosis via ultrasound images. Specifically, we train two simple class-specific Deep Convolutional Generative Adversarial Networks: one for benign and one for malignant lesions. We then simulate a realistic FL setting using three publicly available breast ultrasound image datasets: BUSI, BUS-BRA, and UDIAT. FedAvg and FedProx are adopted as baseline FL algorithms. Experimental results show that incorporating a suitable number of synthetic images improved the average AUC from 0.9206 to 0.9237 for FedAvg and from 0.9429 to 0.9538 for FedProx. We also note that excessive use of synthetic data reduced performance, underscoring the importance of maintaining a balanced ratio of real and synthetic samples. Our findings highlight the potential of generative AI based data augmentation to enhance FL results in the breast ultrasound image classification task.

IntroductionPoint-of-care ultrasonography (POCUS) enables clinicians to obtain critical diagnostic information at the bedside especially in resource limited settings. This information may include 2D cardiac quantitative data, although measuring the data manually can be time-consuming and subject to user experience. Artificial intelligence (AI) can potentially automate this quantification. This study assessed the interpretation of key cardiac measurements on POCUS images by an AI-enabled device (AISAP Cardio V1.0). MethodsThis retrospective diagnostic accuracy study included 200 POCUS cases from four hospitals (two in Israel and two in the United States). Each case was independently interpreted by three cardiologists and the device for seven measurements (left ventricular (LV) ejection fraction, inferior vena cava (IVC) maximal diameter, left atrial (LA) area, right atrial (RA) area, LV end diastolic diameter, right ventricular (RV) fractional area change and aortic root diameter). The endpoints were the root mean square error (RMSE) of the device compared to the average cardiologist measurement (LV ejection fraction and IVC maximal diameter were primary endpoints; the other measurements were secondary endpoints). Predefined passing criteria were based on the upper bounds of the RMSE 95% confidence intervals (CIs). The inter-cardiologist RMSE was also calculated for reference. ResultsThe device achieved the passing criteria for six of the seven measurements. While not achieving the passing criterion for RV fractional area change, it still achieved a better RMSE than the inter-cardiologist RMSE. The RMSE was 6.20% (95% CI: 5.57 to 6.83; inter-cardiologist RMSE of 8.23%) for LV ejection fraction, 0.25cm (95% CI: 0.20 to 0.29; 0.36cm) for IVC maximal diameter, 2.39cm2 (95% CI: 1.96 to 2.82; 4.39cm2) for LA area, 2.11cm2 (95% CI: 1.75 to 2.47; 3.49cm2) for RA area, 5.06mm (95% CI: 4.58 to 5.55; 4.67mm) for LV end diastolic diameter, 10.17% (95% CI: 9.01 to 11.33; 14.12%) for RV fractional area change and 0.19cm (95% CI: 0.16 to 0.21; 0.24cm) for aortic root diameter. DiscussionThe device accurately calculated these cardiac measurements especially when benchmarked against inter-cardiologist variability. Its use could assist clinicians who utilize POCUS and better enable their clinical decision-making.

Transcranial Doppler (TCD) ultrasound has significant clinical value for assessing cerebral hemodynamics, but its reliance on operator expertise limits broader clinical adoption. In this work, we present a lightweight real-time deep learning-based approach capable of automatically identifying the middle cerebral artery (MCA) in TCD Color Doppler images. Two state-of-the-art object detection models, YOLOv10 and Real-Time Detection Transformers (RT-DETR), were investigated for automated MCA detection in real-time. TCD Color Doppler data (41 subjects; 365 videos; 61,611 frames) were collected from neurologically healthy individuals (n = 31) and stroke patients (n = 10). MCA bounding box annotations were performed by clinical experts on all frames. Model training consisted of pretraining utilizing a large abdominal ultrasound dataset followed by subsequent fine-tuning on acquired TCD data. Detection performance at the instance and frame levels, and inference speed were assessed through four-fold cross-validation. Inter-rater agreement between model and two human expert readers was assessed using distance between bounding boxes and inter-rater variability was quantified using the individual equivalence coefficient (IEC) metric. Both YOLOv10 and RT-DETR models showed comparable frame level accuracy for MCA presence, with F1 scores of 0.884 ± 0.023 and 0.884 ± 0.019 respectively. YOLOv10 outperformed RT-DETR for instance-level localization accuracy (AP: 0.817 vs. 0.780) and had considerably faster inference speed on a desktop CPU (11.6 ms vs. 91.14 ms). Furthermore, YOLOv10 showed an average inference time of 36 ms per frame on a tablet device. The IEC was -1.08 with 95 % confidence interval: [-1.45, -0.19], showing that the AI predictions deviated less from each reader than the readers' annotations deviated from each other. Real-time automated detection of the MCA is feasible and can be implemented on mobile platforms, potentially enabling wider clinical adoption by less-trained operators in point-of-care settings.

Infant meningitis can be a life-threatening disease and requires prompt and accurate diagnosis to prevent severe outcomes or death. Gold-standard diagnosis requires lumbar puncture (LP) to obtain and analyze cerebrospinal fluid (CSF). Despite being standard practice, LPs are invasive, pose risks for the patient and often yield negative results, either due to contamination with red blood cells from the puncture itself or because LPs are routinely performed to rule out a life-threatening infection, despite the disease's relatively low incidence. Furthermore, in low-income settings where incidence is the highest, LPs and CSF exams are rarely feasible, and suspected meningitis cases are generally treated empirically. There is a growing need for non-invasive, accurate diagnostic methods. We developed a three-stage deep learning framework using Neosonics ultrasound technology for 30 infants with suspected meningitis and a permeable fontanelle at three Spanish University Hospitals (from 2021 to 2023). In stage 1, 2194 images were processed for quality control using a vessel/non-vessel model, with a focus on vessel identification and manual removal of images exhibiting artifacts such as poor coupling and clutter. This refinement process resulted in a final cohort comprising 16 patients-6 cases (336 images) and 10 controls (445 images), yielding 781 images for the second stage. The second stage involved the use of a deep learning model to classify images based on a white blood cell count threshold (set at 30 cells/mm³) into control or meningitis categories. The third stage integrated explainable artificial intelligence (XAI) methods, such as Grad-CAM visualizations, alongside image statistical analysis, to provide transparency and interpretability of the model's decision-making process in our artificial intelligence-driven screening tool. Our approach achieved 96% accuracy in quality control and 93% precision and 92% accuracy in image-level meningitis detection, with an overall patient-level accuracy of 94%. It identified 6 meningitis cases and 10 controls with 100% sensitivity and 90% specificity, demonstrating only a single misclassification. The use of gradient-weighted class activation mapping-based XAI significantly enhanced diagnostic interpretability, and to further refine our insights we incorporated a statistics-based XAI approach. By analyzing image metrics such as entropy and standard deviation, we identified texture variations in the images attributable to the presence of cells, which improved the interpretability of our diagnostic tool. This study supports the efficacy of a multi-stage deep learning model for non-invasive screening of infant meningitis and its potential to guide the need for LPs. It also highlights the transformative potential of artificial intelligence in medical diagnostic screening for neonatal health care, paving the way for future research and innovations.

Transperineal ultrasound (TPUS) is a widely used tool for evaluating female pelvic organ prolapse (POP), but its accurate interpretation relies on experience, causing diagnostic variability. This study aims to develop and validate a multi-task deep learning model to automate POP assessment using TPUS images. TPUS images from 1340 female patients (January-June 2023) were evaluated by two experienced physicians. The presence and severity of cystocele, uterine prolapse, rectocele, and excessive mobility of perineal body (EMoPB) were documented. After preprocessing, 1072 images were used for training and 268 for validation. The model used ResNet34 as the feature extractor and four parallel fully connected layers to predict the conditions. Model performance was assessed using confusion matrix and area under the curve (AUC). Gradient-weighted class activation mapping (Grad-CAM) visualized the model's focus areas. The model demonstrated strong diagnostic performance, with accuracies and AUC values as follows: cystocele, 0.869 (95% CI, 0.824-0.905) and 0.947 (95% CI, 0.930-0.962); uterine prolapse, 0.799 (95% CI, 0.746-0.842) and 0.931 (95% CI, 0.911-0.948); rectocele, 0.978 (95% CI, 0.952-0.990) and 0.892 (95% CI, 0.849-0.927); and EMoPB, 0.869 (95% CI, 0.824-0.905) and 0.942 (95% CI, 0.907-0.967). Grad-CAM heatmaps revealed that the model's focus areas were consistent with those observed by human experts. This study presents a multi-task deep learning model for automated POP assessment using TPUS images, showing promising efficacy and potential to benefit a broader population of women.

This study aimed to develop a machine learning model using breast ultrasound images to improve the non-invasive differential diagnosis between Sclerosing Adenosis (SA) and Invasive Ductal Carcinoma (IDC). 2046 ultrasound images from 772 SA and IDC patients were collected, Regions of Interest (ROI) were delineated, and features were extracted. The dataset was split into training and test cohorts, and feature selection was performed by correlation coefficients and Recursive Feature Elimination. 10 classifiers with Grid Search and 5-fold cross-validation were applied during model training. Receiver Operating Characteristic (ROC) curve and Youden index were used to model evaluation. SHapley Additive exPlanations (SHAP) was employed for model interpretation. Another 224 ROIs of 84 patients from other hospitals were used for external validation. For the ROI-level model, XGBoost with 18 features achieved an area under the curve (AUC) of 0.9758 (0.9654-0.9847) in the test cohort and 0.9906 (0.9805-0.9973) in the validation cohort. For the patient-level model, logistic regression with 9 features achieved an AUC of 0.9653 (0.9402-0.9859) in the test cohort and 0.9846 (0.9615-0.9978) in the validation cohort. The feature "Original shape Major Axis Length" was identified as the most important, with its value positively correlated with a higher likelihood of the sample being IDC. Feature contributions for specific ROIs were visualized as well. We developed explainable, ultrasound-based machine learning models with high performance for differentiating SA and IDC, offering a potential non-invasive tool for improved differential diagnosis. Question Accurately distinguishing between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) in a non-invasive manner has been a diagnostic challenge. Findings Explainable, ultrasound-based machine learning models with high performance were developed for differentiating SA and IDC, and validated well in external validation cohort. Critical relevance These models provide non-invasive tools to reduce misdiagnoses of SA and improve early detection for IDC.

Intraoperative ultrasound scanning is a demanding visuotactile task. It requires operators to simultaneously localise the ultrasound perspective and manually perform slight adjustments to the pose of the probe, making sure not to apply excessive force or breaking contact with the tissue, while also characterising the visible tissue. To analyse the probe-tissue contact, an iterative filtering and topological method is proposed to identify the underlying visible tissue, which can be used to detect acoustic shadow and construct confidence maps of perceptual salience. For evaluation, datasets containing both in vivo and medical phantom data are created. A suite of evaluations is performed, including an evaluation of acoustic shadow classification. Compared to an ablation, deep learning, and statistical method, the proposed approach achieves superior classification on in vivo data, achieving an <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>F</mi> <mi>β</mi></msub> </math> score of 0.864, in comparison with 0.838, 0.808, and 0.808. A novel framework for evaluating the confidence estimation of probe-tissue contact is created. The phantom data are captured specifically for this, and comparison is made against two established methods. The proposed method produced the superior response, achieving an average normalised root-mean-square error of 0.168, in comparison with 1.836 and 4.542. Evaluation is also extended to determine the algorithm's robustness to parameter perturbation, speckle noise, data distribution shift, and capability for guiding a robotic scan. The results of this comprehensive set of experiments justify the potential clinical value of the proposed algorithm, which can be used to support clinical training and robotic ultrasound automation.